Monitoring Realized Performance for Regression
Note
The following example uses timestamps. These are optional but have an impact on the way data is chunked and results are plotted. You can read more about them in the data requirements.
Just The Code
>>> import nannyml as nml
>>> import pandas as pd
>>> from IPython.display import display
>>> reference_df = nml.load_synthetic_car_price_dataset()[0]
>>> analysis_df = nml.load_synthetic_car_price_dataset()[1]
>>> analysis_targets_df = nml.load_synthetic_car_price_dataset()[2]
>>> analysis_df = pd.merge(analysis_df, analysis_targets_df, on='id')
>>> display(reference_df.head(3))
>>> calc = nml.PerformanceCalculator(
... y_pred='y_pred',
... y_true='y_true',
... timestamp_column_name='timestamp',
... problem_type='regression',
... metrics=['mae', 'mape', 'mse', 'msle', 'rmse', 'rmsle'],
... chunk_size=6000)
>>> calc.fit(reference_df)
>>> results = calc.calculate(analysis_df)
>>> display(results.filter(period='analysis').to_df())
>>> display(results.filter(period='reference').to_df())
>>> figure = results.plot(kind='performance')
>>> figure.show()
Advanced configuration
To learn how
Chunk
works and to set up custom chunkings check out the chunking tutorialTo learn how
ConstantThreshold
works and to set up custom threshold check out the thresholds tutorial
Walkthrough
For simplicity this guide is based on a synthetic dataset included in the library, where the monitored model predicts the market price of a used car. Check out Car Price Dataset to learn more about this dataset.
In order to monitor a model, NannyML needs to learn about it from a reference dataset. Then it can monitor the data that is subject to actual analysis, provided as the analysis dataset. You can read more about this in our section on data periods.
The analysis_targets_df
dataframe contains the target results of the analysis period. This is kept separate in the synthetic data because it is
not used during performance estimation.
But as it is required to calculate performance, the first thing to do in this case is to join the analysis target values with the analysis data.
>>> import nannyml as nml
>>> import pandas as pd
>>> from IPython.display import display
>>> reference_df = nml.load_synthetic_car_price_dataset()[0]
>>> analysis_df = nml.load_synthetic_car_price_dataset()[1]
>>> analysis_targets_df = nml.load_synthetic_car_price_dataset()[2]
>>> analysis_df = pd.merge(analysis_df, analysis_targets_df, on='id')
>>> display(reference_df.head(3))
id |
car_age |
km_driven |
price_new |
accident_count |
door_count |
fuel |
transmission |
y_true |
y_pred |
timestamp |
|
---|---|---|---|---|---|---|---|---|---|---|---|
0 |
0 |
15 |
144020 |
42810 |
4 |
3 |
diesel |
automatic |
569 |
1246 |
2017-01-24 08:00:00.000 |
1 |
1 |
12 |
57078 |
31835 |
3 |
3 |
electric |
automatic |
4277 |
4924 |
2017-01-24 08:00:33.600 |
2 |
2 |
2 |
76288 |
31851 |
3 |
5 |
diesel |
automatic |
7011 |
5744 |
2017-01-24 08:01:07.200 |
Next a PerformanceCalculator
is created using a list of metrics to calculate (or just one metric),
the data columns required for these metrics, an optional chunking specification and the type of machine learning problem being addressed.
The list of metrics specifies which performance metrics of the monitored model will be calculated. The following metrics are currently supported:
mae
- mean absolute errormape
- mean absolute percentage errormse
- mean squared errorrmse
- root mean squared errormsle
- mean squared logarithmic errorrmsle
- root mean squared logarithmic error
For more information on metrics, check the metrics
module.
>>> calc = nml.PerformanceCalculator(
... y_pred='y_pred',
... y_true='y_true',
... timestamp_column_name='timestamp',
... problem_type='regression',
... metrics=['mae', 'mape', 'mse', 'msle', 'rmse', 'rmsle'],
... chunk_size=6000)
>>> calc.fit(reference_df)
The new PerformanceCalculator
is fitted using the
fit()
method on the reference data.
The fitted PerformanceCalculator
can then be used to calculate
realized performance metrics on all data which has target values available with the
calculate()
method.
NannyML can output a dataframe that contains all the results of the analysis data.
>>> results = calc.calculate(analysis_df)
>>> display(results.filter(period='analysis').to_df())
chunk
key
|
chunk_index
|
start_index
|
end_index
|
start_date
|
end_date
|
period
|
targets_missing_rate
|
mae
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
mape
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
mse
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
msle
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
rmse
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
rmsle
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 |
[0:5999] |
0 |
0 |
5999 |
2017-02-16 16:00:00 |
2017-02-18 23:59:26.400000 |
analysis |
0 |
8.21576 |
853.4 |
874.805 |
817.855 |
False |
0.00248466 |
0.228707 |
0.237019 |
0.229456 |
True |
21915 |
1.14313e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0704883 |
0.0737091 |
0.0696521 |
False |
10.348 |
1069.17 |
1103.31 |
1014.28 |
False |
0.002239 |
0.265496 |
0.271511 |
0.263948 |
False |
1 |
[6000:11999] |
1 |
6000 |
11999 |
2017-02-19 00:00:00 |
2017-02-21 07:59:26.400000 |
analysis |
0 |
8.21576 |
853.137 |
874.805 |
817.855 |
False |
0.00248466 |
0.230818 |
0.237019 |
0.229456 |
False |
21915 |
1.13987e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0699896 |
0.0737091 |
0.0696521 |
False |
10.348 |
1067.65 |
1103.31 |
1014.28 |
False |
0.002239 |
0.264556 |
0.271511 |
0.263948 |
False |
2 |
[12000:17999] |
2 |
12000 |
17999 |
2017-02-21 08:00:00 |
2017-02-23 15:59:26.400000 |
analysis |
0 |
8.21576 |
846.304 |
874.805 |
817.855 |
False |
0.00248466 |
0.229042 |
0.237019 |
0.229456 |
True |
21915 |
1.12872e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0696923 |
0.0737091 |
0.0696521 |
False |
10.348 |
1062.41 |
1103.31 |
1014.28 |
False |
0.002239 |
0.263993 |
0.271511 |
0.263948 |
False |
3 |
[18000:23999] |
3 |
18000 |
23999 |
2017-02-23 16:00:00 |
2017-02-25 23:59:26.400000 |
analysis |
0 |
8.21576 |
855.495 |
874.805 |
817.855 |
False |
0.00248466 |
0.233624 |
0.237019 |
0.229456 |
False |
21915 |
1.15829e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0719322 |
0.0737091 |
0.0696521 |
False |
10.348 |
1076.24 |
1103.31 |
1014.28 |
False |
0.002239 |
0.268202 |
0.271511 |
0.263948 |
False |
4 |
[24000:29999] |
4 |
24000 |
29999 |
2017-02-26 00:00:00 |
2017-02-28 07:59:26.400000 |
analysis |
0 |
8.21576 |
849.33 |
874.805 |
817.855 |
False |
0.00248466 |
0.233887 |
0.237019 |
0.229456 |
False |
21915 |
1.12429e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0724877 |
0.0737091 |
0.0696521 |
False |
10.348 |
1060.32 |
1103.31 |
1014.28 |
False |
0.002239 |
0.269235 |
0.271511 |
0.263948 |
False |
5 |
[30000:35999] |
5 |
30000 |
35999 |
2017-02-28 08:00:00 |
2017-03-02 15:59:26.400000 |
analysis |
0 |
8.21576 |
702.518 |
874.805 |
817.855 |
True |
0.00248466 |
0.262864 |
0.237019 |
0.229456 |
True |
21915 |
829589 |
1.21572e+06 |
1.02681e+06 |
True |
0.0011989 |
0.104949 |
0.0737091 |
0.0696521 |
True |
10.348 |
910.818 |
1103.31 |
1014.28 |
True |
0.002239 |
0.323958 |
0.271511 |
0.263948 |
True |
6 |
[36000:41999] |
6 |
36000 |
41999 |
2017-03-02 16:00:00 |
2017-03-04 23:59:26.400000 |
analysis |
0 |
8.21576 |
700.736 |
874.805 |
817.855 |
True |
0.00248466 |
0.26346 |
0.237019 |
0.229456 |
True |
21915 |
829693 |
1.21572e+06 |
1.02681e+06 |
True |
0.0011989 |
0.104814 |
0.0737091 |
0.0696521 |
True |
10.348 |
910.875 |
1103.31 |
1014.28 |
True |
0.002239 |
0.32375 |
0.271511 |
0.263948 |
True |
7 |
[42000:47999] |
7 |
42000 |
47999 |
2017-03-05 00:00:00 |
2017-03-07 07:59:26.400000 |
analysis |
0 |
8.21576 |
684.702 |
874.805 |
817.855 |
True |
0.00248466 |
0.26095 |
0.237019 |
0.229456 |
True |
21915 |
792287 |
1.21572e+06 |
1.02681e+06 |
True |
0.0011989 |
0.104347 |
0.0737091 |
0.0696521 |
True |
10.348 |
890.105 |
1103.31 |
1014.28 |
True |
0.002239 |
0.323027 |
0.271511 |
0.263948 |
True |
8 |
[48000:53999] |
8 |
48000 |
53999 |
2017-03-07 08:00:00 |
2017-03-09 15:59:26.400000 |
analysis |
0 |
8.21576 |
705.814 |
874.805 |
817.855 |
True |
0.00248466 |
0.265371 |
0.237019 |
0.229456 |
True |
21915 |
835917 |
1.21572e+06 |
1.02681e+06 |
True |
0.0011989 |
0.104714 |
0.0737091 |
0.0696521 |
True |
10.348 |
914.285 |
1103.31 |
1014.28 |
True |
0.002239 |
0.323596 |
0.271511 |
0.263948 |
True |
9 |
[54000:59999] |
9 |
54000 |
59999 |
2017-03-09 16:00:00 |
2017-03-11 23:59:26.400000 |
analysis |
0 |
8.21576 |
698.344 |
874.805 |
817.855 |
True |
0.00248466 |
0.265757 |
0.237019 |
0.229456 |
True |
21915 |
825936 |
1.21572e+06 |
1.02681e+06 |
True |
0.0011989 |
0.105882 |
0.0737091 |
0.0696521 |
True |
10.348 |
908.81 |
1103.31 |
1014.28 |
True |
0.002239 |
0.325394 |
0.271511 |
0.263948 |
True |
The results from the reference data are also available.
>>> display(results.filter(period='reference').to_df())
chunk
key
|
chunk_index
|
start_index
|
end_index
|
start_date
|
end_date
|
period
|
targets_missing_rate
|
mae
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
mape
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
mse
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
msle
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
rmse
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
rmsle
sampling_error
|
value
|
upper_threshold
|
lower_threshold
|
alert
|
|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 |
[0:5999] |
0 |
0 |
5999 |
2017-01-24 08:00:00 |
2017-01-26 15:59:26.400000 |
reference |
0 |
8.21576 |
863.932 |
874.805 |
817.855 |
False |
0.00248466 |
0.23274 |
0.237019 |
0.229456 |
False |
21915 |
1.18007e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0715427 |
0.0737091 |
0.0696521 |
False |
10.348 |
1086.31 |
1103.31 |
1014.28 |
False |
0.002239 |
0.267475 |
0.271511 |
0.263948 |
False |
1 |
[6000:11999] |
1 |
6000 |
11999 |
2017-01-26 16:00:00 |
2017-01-28 23:59:26.400000 |
reference |
0 |
8.21576 |
844.491 |
874.805 |
817.855 |
False |
0.00248466 |
0.234282 |
0.237019 |
0.229456 |
False |
21915 |
1.12407e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0721316 |
0.0737091 |
0.0696521 |
False |
10.348 |
1060.22 |
1103.31 |
1014.28 |
False |
0.002239 |
0.268573 |
0.271511 |
0.263948 |
False |
2 |
[12000:17999] |
2 |
12000 |
17999 |
2017-01-29 00:00:00 |
2017-01-31 07:59:26.400000 |
reference |
0 |
8.21576 |
830.578 |
874.805 |
817.855 |
False |
0.00248466 |
0.231986 |
0.237019 |
0.229456 |
False |
21915 |
1.07831e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0709387 |
0.0737091 |
0.0696521 |
False |
10.348 |
1038.42 |
1103.31 |
1014.28 |
False |
0.002239 |
0.266343 |
0.271511 |
0.263948 |
False |
3 |
[18000:23999] |
3 |
18000 |
23999 |
2017-01-31 08:00:00 |
2017-02-02 15:59:26.400000 |
reference |
0 |
8.21576 |
838.746 |
874.805 |
817.855 |
False |
0.00248466 |
0.231618 |
0.237019 |
0.229456 |
False |
21915 |
1.07827e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0709489 |
0.0737091 |
0.0696521 |
False |
10.348 |
1038.4 |
1103.31 |
1014.28 |
False |
0.002239 |
0.266362 |
0.271511 |
0.263948 |
False |
4 |
[24000:29999] |
4 |
24000 |
29999 |
2017-02-02 16:00:00 |
2017-02-04 23:59:26.400000 |
reference |
0 |
8.21576 |
857.765 |
874.805 |
817.855 |
False |
0.00248466 |
0.235091 |
0.237019 |
0.229456 |
False |
21915 |
1.14923e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0727984 |
0.0737091 |
0.0696521 |
False |
10.348 |
1072.02 |
1103.31 |
1014.28 |
False |
0.002239 |
0.269812 |
0.271511 |
0.263948 |
False |
5 |
[30000:35999] |
5 |
30000 |
35999 |
2017-02-05 00:00:00 |
2017-02-07 07:59:26.400000 |
reference |
0 |
8.21576 |
852.697 |
874.805 |
817.855 |
False |
0.00248466 |
0.232364 |
0.237019 |
0.229456 |
False |
21915 |
1.15555e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0712554 |
0.0737091 |
0.0696521 |
False |
10.348 |
1074.97 |
1103.31 |
1014.28 |
False |
0.002239 |
0.266937 |
0.271511 |
0.263948 |
False |
6 |
[36000:41999] |
6 |
36000 |
41999 |
2017-02-07 08:00:00 |
2017-02-09 15:59:26.400000 |
reference |
0 |
8.21576 |
842.253 |
874.805 |
817.855 |
False |
0.00248466 |
0.232789 |
0.237019 |
0.229456 |
False |
21915 |
1.12037e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0715653 |
0.0737091 |
0.0696521 |
False |
10.348 |
1058.48 |
1103.31 |
1014.28 |
False |
0.002239 |
0.267517 |
0.271511 |
0.263948 |
False |
7 |
[42000:47999] |
7 |
42000 |
47999 |
2017-02-09 16:00:00 |
2017-02-11 23:59:26.400000 |
reference |
0 |
8.21576 |
837.9 |
874.805 |
817.855 |
False |
0.00248466 |
0.235516 |
0.237019 |
0.229456 |
False |
21915 |
1.10396e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0729194 |
0.0737091 |
0.0696521 |
False |
10.348 |
1050.7 |
1103.31 |
1014.28 |
False |
0.002239 |
0.270036 |
0.271511 |
0.263948 |
False |
8 |
[48000:53999] |
8 |
48000 |
53999 |
2017-02-12 00:00:00 |
2017-02-14 07:59:26.400000 |
reference |
0 |
8.21576 |
844.266 |
874.805 |
817.855 |
False |
0.00248466 |
0.232423 |
0.237019 |
0.229456 |
False |
21915 |
1.09914e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0711648 |
0.0737091 |
0.0696521 |
False |
10.348 |
1048.4 |
1103.31 |
1014.28 |
False |
0.002239 |
0.266767 |
0.271511 |
0.263948 |
False |
9 |
[54000:59999] |
9 |
54000 |
59999 |
2017-02-14 08:00:00 |
2017-02-16 15:59:26.400000 |
reference |
0 |
8.21576 |
850.673 |
874.805 |
817.855 |
False |
0.00248466 |
0.233561 |
0.237019 |
0.229456 |
False |
21915 |
1.12369e+06 |
1.21572e+06 |
1.02681e+06 |
False |
0.0011989 |
0.0715405 |
0.0737091 |
0.0696521 |
False |
10.348 |
1060.04 |
1103.31 |
1014.28 |
False |
0.002239 |
0.267471 |
0.271511 |
0.263948 |
False |
Apart from chunking and chunk and period-related columns, the results data have a set of columns for each calculated metric.
targets_missing_rate - The fraction of missing target data.
value - the realized metric value for a specific chunk.
sampling_error - the estimate of the Sampling Error.
upper_threshold and lower_threshold - crossing these thresholds will raise an alert on significant performance change. The thresholds are calculated based on the actual performance of the monitored model on chunks in the reference partition. The thresholds are 3 standard deviations away from the mean performance calculated on chunks. They are calculated during
fit
phase. You can also set up custom thresholds using constant or standard deviations thresholds, to learn more about it check out our tutorial on thresholds.alert - flag indicating potentially significant performance change.
True
if estimated performance crosses upper or lower threshold.
The results can be plotted for visual inspection:
>>> figure = results.plot(kind='performance')
>>> figure.show()
Insights
From looking at the RMSE and RMSLE performance results we can observe an interesting effect. We know that RMSE penalizes mispredictions symmetrically while RMSLE penalizes underprediction more than overprediction. Hence while our model has become a little bit more accurate according to RMSE, the increase in RMSLE tells us that our model is now underpredicting more than it was before!
What Next
If we decide further investigation is needed, the Data Drift functionality can help us to see what feature changes may be contributing to any performance changes. We can also plot the realized performance and compare it with the estimated results.
It is also wise to check whether the model’s performance is satisfactory according to business requirements. This is an ad-hoc investigation that is not covered by NannyML.